Overview

Dataset statistics

Number of variables15
Number of observations2351
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory349.2 KiB
Average record size in memory152.1 B

Variable types

Numeric10
Categorical5

Alerts

seller_name has a high cardinality: 1617 distinct valuesHigh cardinality
title has a high cardinality: 2295 distinct valuesHigh cardinality
comment has a high cardinality: 1912 distinct valuesHigh cardinality
item_approx_price is highly overall correlated with item_primary_priceHigh correlation
accurate_description is highly overall correlated with communication and 3 other fieldsHigh correlation
communication is highly overall correlated with accurate_description and 3 other fieldsHigh correlation
shipping_speed is highly overall correlated with accurate_description and 3 other fieldsHigh correlation
feedback_pr is highly overall correlated with kmeans_clusterHigh correlation
item_primary_price is highly overall correlated with item_approx_priceHigh correlation
reasonable_shipping_cost is highly overall correlated with kmeans_cluster and 1 other fieldsHigh correlation
no_of_comments is highly overall correlated with kmeans_cluster and 1 other fieldsHigh correlation
kmeans_cluster is highly overall correlated with accurate_description and 6 other fieldsHigh correlation
agglomerative_cluster is highly overall correlated with accurate_description and 5 other fieldsHigh correlation
title is uniformly distributedUniform
item_no has unique valuesUnique
item_approx_price has 256 (10.9%) zerosZeros
accurate_description has 291 (12.4%) zerosZeros
communication has 291 (12.4%) zerosZeros
shipping_speed has 291 (12.4%) zerosZeros
feedback_pr has 1073 (45.6%) zerosZeros
review_count has 2191 (93.2%) zerosZeros
reasonable_shipping_cost has 295 (12.5%) zerosZeros

Reproduction

Analysis started2023-10-27 20:42:35.967300
Analysis finished2023-10-27 20:42:49.326716
Duration13.36 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

item_approx_price
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct954
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4176726
Minimum0
Maximum10.618886
Zeros256
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-10-27T16:42:49.996040image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.6823905
median3.5576312
Q34.4164281
95-th percentile5.8473632
Maximum10.618886
Range10.618886
Interquartile range (IQR)1.7340376

Descriptive statistics

Standard deviation1.6811043
Coefficient of variation (CV)0.49188571
Kurtosis0.26378629
Mean3.4176726
Median Absolute Deviation (MAD)0.87524071
Skewness-0.37612161
Sum8034.9484
Variance2.8261118
MonotonicityNot monotonic
2023-10-27T16:42:50.126298image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 256
 
10.9%
4.016743316 33
 
1.4%
3.341093458 31
 
1.3%
3.065258336 28
 
1.2%
3.557631168 27
 
1.1%
2.215937286 26
 
1.1%
3.341801172 25
 
1.1%
2.682390454 25
 
1.1%
3.735047135 25
 
1.1%
3.557346064 23
 
1.0%
Other values (944) 1852
78.8%
ValueCountFrequency (%)
0 256
10.9%
0.009950330853 1
 
< 0.1%
0.8285518176 3
 
0.1%
0.8544153282 9
 
0.4%
0.9439058989 4
 
0.2%
0.9593502213 4
 
0.2%
0.993251773 1
 
< 0.1%
1.01523068 2
 
0.1%
1.01884732 1
 
< 0.1%
1.061256502 1
 
< 0.1%
ValueCountFrequency (%)
10.61888559 1
< 0.1%
8.827248115 1
< 0.1%
8.484818749 1
< 0.1%
8.336905565 1
< 0.1%
8.299718541 1
< 0.1%
7.965882322 1
< 0.1%
7.857267998 1
< 0.1%
7.832704418 1
< 0.1%
7.747545024 2
0.1%
7.687502085 1
< 0.1%

accurate_description
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2922161
Minimum0
Maximum5
Zeros291
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-10-27T16:42:50.222269image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.8
median4.9
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation1.6170722
Coefficient of variation (CV)0.37674528
Kurtosis3.1786538
Mean4.2922161
Median Absolute Deviation (MAD)0.1
Skewness-2.2673635
Sum10091
Variance2.6149224
MonotonicityNot monotonic
2023-10-27T16:42:50.332068image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4.9 946
40.2%
5 696
29.6%
0 291
 
12.4%
4.8 248
 
10.5%
4.7 100
 
4.3%
4.6 43
 
1.8%
4.5 11
 
0.5%
4.4 6
 
0.3%
4.2 4
 
0.2%
4.3 4
 
0.2%
Other values (2) 2
 
0.1%
ValueCountFrequency (%)
0 291
12.4%
3.7 1
 
< 0.1%
3.8 1
 
< 0.1%
4.2 4
 
0.2%
4.3 4
 
0.2%
4.4 6
 
0.3%
4.5 11
 
0.5%
4.6 43
 
1.8%
4.7 100
 
4.3%
4.8 248
10.5%
ValueCountFrequency (%)
5 696
29.6%
4.9 946
40.2%
4.8 248
 
10.5%
4.7 100
 
4.3%
4.6 43
 
1.8%
4.5 11
 
0.5%
4.4 6
 
0.3%
4.3 4
 
0.2%
4.2 4
 
0.2%
3.8 1
 
< 0.1%

seller_name
Categorical

Distinct1617
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Memory size101.3 KiB
Jewellery-exports
 
31
cecraig
 
28
pearlwholesale
 
28
wholesalejewelryer
 
24
romanticism2012
 
20
Other values (1612)
2220 

Length

Max length35
Median length29
Mean length13.521055
Min length3

Characters and Unicode

Total characters31788
Distinct characters76
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1377 ?
Unique (%)58.6%

Sample

1st rowhealth-solution-prime
2nd rowhealth-solution-prime
3rd rowhealth-solution-prime
4th rowhealth-solution-prime
5th rowhealth-solution-prime

Common Values

ValueCountFrequency (%)
Jewellery-exports 31
 
1.3%
cecraig 28
 
1.2%
pearlwholesale 28
 
1.2%
wholesalejewelryer 24
 
1.0%
romanticism2012 20
 
0.9%
Nature Supplements Store 20
 
0.9%
health-solution-prime 17
 
0.7%
Trading Chiefs 14
 
0.6%
super-diag 14
 
0.6%
doubtfu-16 13
 
0.6%
Other values (1607) 2142
91.1%

Length

2023-10-27T16:42:50.447253image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
store 63
 
1.8%
and 44
 
1.3%
the 37
 
1.1%
jewellery-exports 31
 
0.9%
pearlwholesale 28
 
0.8%
cecraig 28
 
0.8%
wholesalejewelryer 24
 
0.7%
shop 24
 
0.7%
nature 21
 
0.6%
trading 21
 
0.6%
Other values (1988) 3186
90.8%

Most occurring characters

ValueCountFrequency (%)
e 2910
 
9.2%
a 2286
 
7.2%
o 1794
 
5.6%
r 1768
 
5.6%
s 1732
 
5.4%
i 1713
 
5.4%
l 1620
 
5.1%
t 1479
 
4.7%
n 1361
 
4.3%
1156
 
3.6%
Other values (66) 13969
43.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24424
76.8%
Uppercase Letter 3323
 
10.5%
Decimal Number 2041
 
6.4%
Space Separator 1156
 
3.6%
Dash Punctuation 358
 
1.1%
Connector Punctuation 319
 
1.0%
Other Punctuation 153
 
0.5%
Final Punctuation 8
 
< 0.1%
Math Symbol 3
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2910
11.9%
a 2286
 
9.4%
o 1794
 
7.3%
r 1768
 
7.2%
s 1732
 
7.1%
i 1713
 
7.0%
l 1620
 
6.6%
t 1479
 
6.1%
n 1361
 
5.6%
c 904
 
3.7%
Other values (16) 6857
28.1%
Uppercase Letter
ValueCountFrequency (%)
S 369
 
11.1%
T 259
 
7.8%
C 255
 
7.7%
A 195
 
5.9%
R 188
 
5.7%
E 174
 
5.2%
L 153
 
4.6%
P 148
 
4.5%
O 140
 
4.2%
N 139
 
4.2%
Other values (16) 1303
39.2%
Decimal Number
ValueCountFrequency (%)
1 333
16.3%
2 326
16.0%
0 288
14.1%
8 206
10.1%
6 176
8.6%
9 163
8.0%
4 162
7.9%
3 155
7.6%
7 141
6.9%
5 91
 
4.5%
Other Punctuation
ValueCountFrequency (%)
' 66
43.1%
. 55
35.9%
& 22
 
14.4%
/ 5
 
3.3%
* 3
 
2.0%
, 2
 
1.3%
Space Separator
ValueCountFrequency (%)
1156
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 358
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 319
100.0%
Final Punctuation
ValueCountFrequency (%)
8
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27747
87.3%
Common 4041
 
12.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2910
 
10.5%
a 2286
 
8.2%
o 1794
 
6.5%
r 1768
 
6.4%
s 1732
 
6.2%
i 1713
 
6.2%
l 1620
 
5.8%
t 1479
 
5.3%
n 1361
 
4.9%
c 904
 
3.3%
Other values (42) 10180
36.7%
Common
ValueCountFrequency (%)
1156
28.6%
- 358
 
8.9%
1 333
 
8.2%
2 326
 
8.1%
_ 319
 
7.9%
0 288
 
7.1%
8 206
 
5.1%
6 176
 
4.4%
9 163
 
4.0%
4 162
 
4.0%
Other values (14) 554
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31779
> 99.9%
Punctuation 8
 
< 0.1%
Letterlike Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2910
 
9.2%
a 2286
 
7.2%
o 1794
 
5.6%
r 1768
 
5.6%
s 1732
 
5.5%
i 1713
 
5.4%
l 1620
 
5.1%
t 1479
 
4.7%
n 1361
 
4.3%
1156
 
3.6%
Other values (64) 13960
43.9%
Punctuation
ValueCountFrequency (%)
8
100.0%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%

communication
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3349213
Minimum0
Maximum5
Zeros291
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-10-27T16:42:50.543316image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.9
median5
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation1.632083
Coefficient of variation (CV)0.37649657
Kurtosis3.1940721
Mean4.3349213
Median Absolute Deviation (MAD)0
Skewness-2.2732076
Sum10191.4
Variance2.6636949
MonotonicityNot monotonic
2023-10-27T16:42:50.633944image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 1322
56.2%
4.9 557
23.7%
0 291
 
12.4%
4.8 89
 
3.8%
4.7 50
 
2.1%
4.6 27
 
1.1%
4.5 7
 
0.3%
4.4 6
 
0.3%
4.3 1
 
< 0.1%
3.5 1
 
< 0.1%
ValueCountFrequency (%)
0 291
 
12.4%
3.5 1
 
< 0.1%
4.3 1
 
< 0.1%
4.4 6
 
0.3%
4.5 7
 
0.3%
4.6 27
 
1.1%
4.7 50
 
2.1%
4.8 89
 
3.8%
4.9 557
23.7%
5 1322
56.2%
ValueCountFrequency (%)
5 1322
56.2%
4.9 557
23.7%
4.8 89
 
3.8%
4.7 50
 
2.1%
4.6 27
 
1.1%
4.5 7
 
0.3%
4.4 6
 
0.3%
4.3 1
 
< 0.1%
3.5 1
 
< 0.1%
0 291
 
12.4%

shipping_speed
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3403233
Minimum0
Maximum5
Zeros291
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-10-27T16:42:50.744648image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.9
median5
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation1.6337006
Coefficient of variation (CV)0.37640067
Kurtosis3.2000637
Mean4.3403233
Median Absolute Deviation (MAD)0
Skewness-2.2753425
Sum10204.1
Variance2.6689776
MonotonicityNot monotonic
2023-10-27T16:42:50.839992image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5 1377
58.6%
4.9 524
 
22.3%
0 291
 
12.4%
4.8 99
 
4.2%
4.7 34
 
1.4%
4.6 15
 
0.6%
4.5 5
 
0.2%
4.1 3
 
0.1%
4.4 1
 
< 0.1%
4.3 1
 
< 0.1%
ValueCountFrequency (%)
0 291
12.4%
4 1
 
< 0.1%
4.1 3
 
0.1%
4.3 1
 
< 0.1%
4.4 1
 
< 0.1%
4.5 5
 
0.2%
4.6 15
 
0.6%
4.7 34
 
1.4%
4.8 99
 
4.2%
4.9 524
22.3%
ValueCountFrequency (%)
5 1377
58.6%
4.9 524
 
22.3%
4.8 99
 
4.2%
4.7 34
 
1.4%
4.6 15
 
0.6%
4.5 5
 
0.2%
4.4 1
 
< 0.1%
4.3 1
 
< 0.1%
4.1 3
 
0.1%
4 1
 
< 0.1%

title
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct2295
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Memory size101.3 KiB
SEIKO 5 AUTOMATIC STEEL SILVER DAY/DATE VINTAGE MEN'S WRIST WATCH
 
4
Original USB-C Charger Cable Charging Cord For iPhone 6 7 8 10 11 12 13 pro Max
 
4
Authentic Ancient Native American Pre Historic Hardin Point Arrowhead Artifact
 
4
Throw - Alpaca wool throw Blanket | Baby Alpaca Throw Blanket | wool throw
 
3
Ancient Bronze Viking Arrow Norse weapon artifact Historical Weapon of War
 
3
Other values (2290)
2333 

Length

Max length91
Median length86
Mean length70.713313
Min length6

Characters and Unicode

Total characters166247
Distinct characters157
Distinct categories20 ?
Distinct scripts6 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2251 ?
Unique (%)95.7%

Sample

1st rowVitamins And Dietary Supplements - Muscle Builder XXL - more muscle growth - 1 B
2nd rowVitamins And Dietary Supplements - MALE VIRILITY - formula works well - 1 Bottle
3rd rowVitamins And Dietary Supplements - GARCINIA CAMBOGIA -Manage cortisol - 1 B
4th rowVitamins And Dietary Supplements - CREATINE POWDER - INSIDE Your Body - 1 B, 100
5th rowVitamins And Dietary Supplements - ELK VELVET ANTLER - overall wellness - 1 B,60

Common Values

ValueCountFrequency (%)
SEIKO 5 AUTOMATIC STEEL SILVER DAY/DATE VINTAGE MEN'S WRIST WATCH 4
 
0.2%
Original USB-C Charger Cable Charging Cord For iPhone 6 7 8 10 11 12 13 pro Max 4
 
0.2%
Authentic Ancient Native American Pre Historic Hardin Point Arrowhead Artifact 4
 
0.2%
Throw - Alpaca wool throw Blanket | Baby Alpaca Throw Blanket | wool throw 3
 
0.1%
Ancient Bronze Viking Arrow Norse weapon artifact Historical Weapon of War 3
 
0.1%
LATTAFA YARA PERFUME FOR WOMEN 100 ML EDP | 100% Original | Female Long Lasting 3
 
0.1%
USB-C to USB-C Cable Fast Charger Type C to Type C Charging Cord Rapid Charger 3
 
0.1%
Custom Gaming Desktop PC Intel i7 Quad 16 GB SSD + 1TB Nvidia GTX 660 2 GB HDMI 3
 
0.1%
Hmt Pilot Hand Winding Men's Steel 17 Jewels Vintage Working Wrist Watch 3
 
0.1%
Authentic Pre Historic Native American Hardin stemmed arrowhead point 2
 
0.1%
Other values (2285) 2319
98.6%

Length

2023-10-27T16:42:50.957996image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
764
 
2.8%
new 437
 
1.6%
for 302
 
1.1%
and 203
 
0.8%
blanket 194
 
0.7%
size 179
 
0.7%
throw 178
 
0.7%
vintage 174
 
0.6%
black 173
 
0.6%
car 172
 
0.6%
Other values (6059) 24220
89.7%

Most occurring characters

ValueCountFrequency (%)
24814
 
14.9%
e 11998
 
7.2%
a 9113
 
5.5%
r 7916
 
4.8%
i 7672
 
4.6%
o 7638
 
4.6%
n 7506
 
4.5%
t 7102
 
4.3%
l 5463
 
3.3%
s 4682
 
2.8%
Other values (147) 72343
43.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 95644
57.5%
Uppercase Letter 33802
 
20.3%
Space Separator 24822
 
14.9%
Decimal Number 8372
 
5.0%
Other Punctuation 1866
 
1.1%
Dash Punctuation 1057
 
0.6%
Open Punctuation 172
 
0.1%
Close Punctuation 169
 
0.1%
Math Symbol 116
 
0.1%
Final Punctuation 105
 
0.1%
Other values (10) 122
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11998
12.5%
a 9113
 
9.5%
r 7916
 
8.3%
i 7672
 
8.0%
o 7638
 
8.0%
n 7506
 
7.8%
t 7102
 
7.4%
l 5463
 
5.7%
s 4682
 
4.9%
d 3180
 
3.3%
Other values (29) 23374
24.4%
Uppercase Letter
ValueCountFrequency (%)
S 3277
 
9.7%
C 2675
 
7.9%
A 2420
 
7.2%
B 2087
 
6.2%
P 2034
 
6.0%
M 1893
 
5.6%
T 1672
 
4.9%
E 1623
 
4.8%
D 1618
 
4.8%
R 1592
 
4.7%
Other values (19) 12911
38.2%
Other Punctuation
ValueCountFrequency (%)
. 429
23.0%
/ 371
19.9%
, 285
15.3%
" 237
12.7%
' 162
 
8.7%
& 99
 
5.3%
! 82
 
4.4%
* 77
 
4.1%
# 52
 
2.8%
: 29
 
1.6%
Other values (9) 43
 
2.3%
Other Symbol
ValueCountFrequency (%)
🔥 13
19.4%
💎 11
16.4%
11
16.4%
° 9
13.4%
® 3
 
4.5%
🥇 3
 
4.5%
2
 
3.0%
🦋 2
 
3.0%
🏆 2
 
3.0%
💖 2
 
3.0%
Other values (9) 9
13.4%
Decimal Number
ValueCountFrequency (%)
0 1570
18.8%
1 1565
18.7%
2 1238
14.8%
5 860
10.3%
3 635
7.6%
6 607
 
7.3%
4 584
 
7.0%
8 445
 
5.3%
9 442
 
5.3%
7 426
 
5.1%
Other Letter
ValueCountFrequency (%)
5
38.5%
2
 
15.4%
2
 
15.4%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
Nonspacing Mark
ValueCountFrequency (%)
3
33.3%
2
22.2%
2
22.2%
1
 
11.1%
1
 
11.1%
Math Symbol
ValueCountFrequency (%)
| 49
42.2%
+ 48
41.4%
~ 18
 
15.5%
× 1
 
0.9%
Open Punctuation
ValueCountFrequency (%)
( 154
89.5%
[ 16
 
9.3%
{ 2
 
1.2%
Close Punctuation
ValueCountFrequency (%)
) 151
89.3%
] 16
 
9.5%
} 2
 
1.2%
Spacing Mark
ValueCountFrequency (%)
2
40.0%
2
40.0%
1
20.0%
Modifier Symbol
ValueCountFrequency (%)
^ 2
40.0%
` 2
40.0%
´ 1
20.0%
Space Separator
ValueCountFrequency (%)
24814
> 99.9%
  8
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 1051
99.4%
6
 
0.6%
Final Punctuation
ValueCountFrequency (%)
81
77.1%
24
 
22.9%
Initial Punctuation
ValueCountFrequency (%)
9
81.8%
2
 
18.2%
Format
ValueCountFrequency (%)
 1
50.0%
1
50.0%
Currency Symbol
ValueCountFrequency (%)
$ 8
100.0%
Other Number
ValueCountFrequency (%)
¼ 1
100.0%
Private Use
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 129424
77.9%
Common 36773
 
22.1%
Khmer 24
 
< 0.1%
Cyrillic 22
 
< 0.1%
Inherited 3
 
< 0.1%
Unknown 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
24814
67.5%
0 1570
 
4.3%
1 1565
 
4.3%
2 1238
 
3.4%
- 1051
 
2.9%
5 860
 
2.3%
3 635
 
1.7%
6 607
 
1.7%
4 584
 
1.6%
8 445
 
1.2%
Other values (63) 3404
 
9.3%
Latin
ValueCountFrequency (%)
e 11998
 
9.3%
a 9113
 
7.0%
r 7916
 
6.1%
i 7672
 
5.9%
o 7638
 
5.9%
n 7506
 
5.8%
t 7102
 
5.5%
l 5463
 
4.2%
s 4682
 
3.6%
S 3277
 
2.5%
Other values (46) 57057
44.1%
Khmer
ValueCountFrequency (%)
5
20.8%
2
 
8.3%
2
 
8.3%
2
 
8.3%
2
 
8.3%
2
 
8.3%
2
 
8.3%
1
 
4.2%
1
 
4.2%
1
 
4.2%
Other values (4) 4
16.7%
Cyrillic
ValueCountFrequency (%)
а 4
18.2%
к 3
13.6%
р 2
9.1%
у 2
9.1%
ш 2
9.1%
г 2
9.1%
я 2
9.1%
е 1
 
4.5%
Ч 1
 
4.5%
И 1
 
4.5%
Other values (2) 2
9.1%
Inherited
ValueCountFrequency (%)
3
100.0%
Unknown
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 165971
99.8%
Punctuation 128
 
0.1%
None 82
 
< 0.1%
Khmer 24
 
< 0.1%
Cyrillic 22
 
< 0.1%
Misc Symbols 11
 
< 0.1%
VS 3
 
< 0.1%
Letterlike Symbols 3
 
< 0.1%
Dingbats 1
 
< 0.1%
Specials 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24814
 
15.0%
e 11998
 
7.2%
a 9113
 
5.5%
r 7916
 
4.8%
i 7672
 
4.6%
o 7638
 
4.6%
n 7506
 
4.5%
t 7102
 
4.3%
l 5463
 
3.3%
s 4682
 
2.8%
Other values (80) 72067
43.4%
Punctuation
ValueCountFrequency (%)
81
63.3%
24
 
18.8%
9
 
7.0%
6
 
4.7%
3
 
2.3%
2
 
1.6%
1
 
0.8%
1
 
0.8%
1
 
0.8%
None
ValueCountFrequency (%)
🔥 13
15.9%
é 11
13.4%
💎 11
13.4%
° 9
11.0%
  8
9.8%
® 3
 
3.7%
🥇 3
 
3.7%
à 2
 
2.4%
â 2
 
2.4%
è 2
 
2.4%
Other values (15) 18
22.0%
Misc Symbols
ValueCountFrequency (%)
11
100.0%
Khmer
ValueCountFrequency (%)
5
20.8%
2
 
8.3%
2
 
8.3%
2
 
8.3%
2
 
8.3%
2
 
8.3%
2
 
8.3%
1
 
4.2%
1
 
4.2%
1
 
4.2%
Other values (4) 4
16.7%
Cyrillic
ValueCountFrequency (%)
а 4
18.2%
к 3
13.6%
р 2
9.1%
у 2
9.1%
ш 2
9.1%
г 2
9.1%
я 2
9.1%
е 1
 
4.5%
Ч 1
 
4.5%
И 1
 
4.5%
Other values (2) 2
9.1%
VS
ValueCountFrequency (%)
3
100.0%
Letterlike Symbols
ValueCountFrequency (%)
2
66.7%
1
33.3%
Dingbats
ValueCountFrequency (%)
1
100.0%
Specials
ValueCountFrequency (%)
1
100.0%
PUA
ValueCountFrequency (%)
1
100.0%

feedback_pr
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4968049
Minimum0
Maximum4.6141299
Zeros1073
Zeros (%)45.6%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-10-27T16:42:51.112470image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.5612183
Q34.6021657
95-th percentile4.6121458
Maximum4.6141299
Range4.6141299
Interquartile range (IQR)4.6021657

Descriptive statistics

Standard deviation2.2884227
Coefficient of variation (CV)0.91654043
Kurtosis-1.9706885
Mean2.4968049
Median Absolute Deviation (MAD)0.051920057
Skewness-0.17483875
Sum5869.9884
Variance5.2368783
MonotonicityNot monotonic
2023-10-27T16:42:51.233416image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1073
45.6%
4.610157727 83
 
3.5%
4.599152114 69
 
2.9%
4.606169686 63
 
2.7%
4.613138356 59
 
2.5%
4.598145571 57
 
2.4%
4.6121458 57
 
2.4%
4.608165695 55
 
2.3%
4.607168189 53
 
2.3%
4.611152258 51
 
2.2%
Other values (77) 731
31.1%
ValueCountFrequency (%)
0 1073
45.6%
4.21508618 4
 
0.2%
4.293195421 1
 
< 0.1%
4.434381865 3
 
0.1%
4.447346101 1
 
< 0.1%
4.449685283 1
 
< 0.1%
4.455509411 1
 
< 0.1%
4.456670178 1
 
< 0.1%
4.462453884 4
 
0.2%
4.470495283 2
 
0.1%
ValueCountFrequency (%)
4.614129927 44
1.9%
4.613138356 59
2.5%
4.6121458 57
2.4%
4.611152258 51
2.2%
4.610157727 83
3.5%
4.609162207 44
1.9%
4.608165695 55
2.3%
4.607168189 53
2.3%
4.606169686 63
2.7%
4.604169686 28
 
1.2%

item_primary_price
Real number (ℝ)

Distinct1014
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5231737
Minimum0.0099503309
Maximum10.308986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-10-27T16:42:51.372838image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.0099503309
5-th percentile1.2570385
Q12.6751811
median3.4336646
Q34.3306018
95-th percentile5.8607862
Maximum10.308986
Range10.299036
Interquartile range (IQR)1.6554206

Descriptive statistics

Standard deviation1.350105
Coefficient of variation (CV)0.38320704
Kurtosis0.70618456
Mean3.5231737
Median Absolute Deviation (MAD)0.82887445
Skewness0.44268268
Sum8282.9814
Variance1.8227834
MonotonicityNot monotonic
2023-10-27T16:42:51.499987image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.713328135 33
 
1.4%
3.044046134 31
 
1.3%
1.944480556 29
 
1.2%
2.771963527 29
 
1.2%
3.258096538 28
 
1.2%
2.396985768 27
 
1.1%
3.044522438 27
 
1.1%
3.433664572 26
 
1.1%
2.772588722 23
 
1.0%
3.257711849 23
 
1.0%
Other values (1004) 2075
88.3%
ValueCountFrequency (%)
0.009950330853 1
 
< 0.1%
0.6881346387 13
0.6%
0.7654678421 1
 
< 0.1%
0.7793248768 5
 
0.2%
0.8020015855 2
 
0.1%
0.8109302162 2
 
0.1%
0.8197798315 1
 
< 0.1%
0.8285518176 2
 
0.1%
0.8329091229 1
 
< 0.1%
0.8415671857 2
 
0.1%
ValueCountFrequency (%)
10.30898599 1
< 0.1%
9.107988659 1
< 0.1%
8.824824939 1
< 0.1%
8.517393171 1
< 0.1%
8.2238337 1
< 0.1%
8.174984533 1
< 0.1%
8.160643954 1
< 0.1%
8.027084809 1
< 0.1%
7.989899375 2
0.1%
7.824445931 1
< 0.1%

review_count
Real number (ℝ)

Distinct69
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1293067
Minimum0
Maximum1105
Zeros2191
Zeros (%)93.2%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-10-27T16:42:51.638682image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum1105
Range1105
Interquartile range (IQR)0

Descriptive statistics

Standard deviation51.746751
Coefficient of variation (CV)10.088449
Kurtosis271.89759
Mean5.1293067
Median Absolute Deviation (MAD)0
Skewness15.680362
Sum12059
Variance2677.7263
MonotonicityNot monotonic
2023-10-27T16:42:51.766230image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2191
93.2%
1 19
 
0.8%
4 12
 
0.5%
5 9
 
0.4%
3 9
 
0.4%
2 6
 
0.3%
22 5
 
0.2%
10 5
 
0.2%
11 5
 
0.2%
6 3
 
0.1%
Other values (59) 87
 
3.7%
ValueCountFrequency (%)
0 2191
93.2%
1 19
 
0.8%
2 6
 
0.3%
3 9
 
0.4%
4 12
 
0.5%
5 9
 
0.4%
6 3
 
0.1%
7 3
 
0.1%
8 3
 
0.1%
9 3
 
0.1%
ValueCountFrequency (%)
1105 1
< 0.1%
986 1
< 0.1%
962 1
< 0.1%
844 1
< 0.1%
769 2
0.1%
608 1
< 0.1%
418 1
< 0.1%
392 2
0.1%
239 1
< 0.1%
211 1
< 0.1%

reasonable_shipping_cost
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2990642
Minimum0
Maximum5
Zeros295
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-10-27T16:42:51.861734image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.8
median4.9
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation1.6328852
Coefficient of variation (CV)0.3798234
Kurtosis3.0644268
Mean4.2990642
Median Absolute Deviation (MAD)0.1
Skewness-2.2406744
Sum10107.1
Variance2.666314
MonotonicityNot monotonic
2023-10-27T16:42:51.950212image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5 1164
49.5%
4.9 403
 
17.1%
0 295
 
12.5%
4.8 294
 
12.5%
4.7 103
 
4.4%
4.6 52
 
2.2%
4.5 26
 
1.1%
4.4 10
 
0.4%
4.3 2
 
0.1%
4.1 1
 
< 0.1%
ValueCountFrequency (%)
0 295
12.5%
4.1 1
 
< 0.1%
4.2 1
 
< 0.1%
4.3 2
 
0.1%
4.4 10
 
0.4%
4.5 26
 
1.1%
4.6 52
 
2.2%
4.7 103
 
4.4%
4.8 294
12.5%
4.9 403
17.1%
ValueCountFrequency (%)
5 1164
49.5%
4.9 403
 
17.1%
4.8 294
 
12.5%
4.7 103
 
4.4%
4.6 52
 
2.2%
4.5 26
 
1.1%
4.4 10
 
0.4%
4.3 2
 
0.1%
4.2 1
 
< 0.1%
4.1 1
 
< 0.1%

item_no
Real number (ℝ)

Distinct2351
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6487085 × 1011
Minimum1.1170102 × 1011
Maximum4.0455526 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-10-27T16:42:52.066969image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1.1170102 × 1011
5-th percentile1.2565119 × 1011
Q11.7596771 × 1011
median2.7459813 × 1011
Q33.535622 × 1011
95-th percentile4.0385633 × 1011
Maximum4.0455526 × 1011
Range2.9285424 × 1011
Interquartile range (IQR)1.7759449 × 1011

Descriptive statistics

Standard deviation9.2069061 × 1010
Coefficient of variation (CV)0.34759983
Kurtosis-1.2974828
Mean2.6487085 × 1011
Median Absolute Deviation (MAD)8.8480526 × 1010
Skewness-0.061351102
Sum6.2271136 × 1014
Variance8.476712 × 1021
MonotonicityNot monotonic
2023-10-27T16:42:52.191877image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.349989223 × 10111
 
< 0.1%
1.861248342 × 10111
 
< 0.1%
4.044313055 × 10111
 
< 0.1%
3.942990268 × 10111
 
< 0.1%
1.856732961 × 10111
 
< 0.1%
1.447777743 × 10111
 
< 0.1%
2.745981283 × 10111
 
< 0.1%
1.44782315 × 10111
 
< 0.1%
4.045082157 × 10111
 
< 0.1%
1.260840288 × 10111
 
< 0.1%
Other values (2341) 2341
99.6%
ValueCountFrequency (%)
1.117010242 × 10111
< 0.1%
1.123060187 × 10111
< 0.1%
1.138835531 × 10111
< 0.1%
1.138931059 × 10111
< 0.1%
1.139042713 × 10111
< 0.1%
1.139264875 × 10111
< 0.1%
1.139336512 × 10111
< 0.1%
1.139761862 × 10111
< 0.1%
1.14123478 × 10111
< 0.1%
1.141527398 × 10111
< 0.1%
ValueCountFrequency (%)
4.045552594 × 10111
< 0.1%
4.045552507 × 10111
< 0.1%
4.04555237 × 10111
< 0.1%
4.045552226 × 10111
< 0.1%
4.045551883 × 10111
< 0.1%
4.045539262 × 10111
< 0.1%
4.045539218 × 10111
< 0.1%
4.045533496 × 10111
< 0.1%
4.045528922 × 10111
< 0.1%
4.045527349 × 10111
< 0.1%

comment
Categorical

Distinct1912
Distinct (%)81.3%
Missing0
Missing (%)0.0%
Memory size101.3 KiB
0
 
106
Great vitamin whith the best price aaaaaaaaaaaaaaaaaaaaaa&&
 
20
I like it a whole lot&Very good seller, fast shipping,recommend&Really liked the products
 
18
Perfect fit Arrived early than expected&Perfect fit Arrived early than expected&Perfect fit Arrived early than expected
 
17
Excellent&Never received the product nor communication fro seller&Fast shipping
 
14
Other values (1907)
2176 

Length

Max length1332
Median length523
Mean length175.38367
Min length1

Characters and Unicode

Total characters412327
Distinct characters170
Distinct categories7 ?
Distinct scripts8 ?
Distinct blocks9 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1777 ?
Unique (%)75.6%

Sample

1st rowarrived on time&Excellent&Never received the product nor communication fro seller&Fast shipping
2nd rowExcellent&Never received the product nor communication fro seller&Fast shipping
3rd rowExcellent&Never received the product nor communication fro seller&Fast shipping
4th rowExcellent&Never received the product nor communication fro seller&Fast shipping
5th rowExcellent&Never received the product nor communication fro seller&Fast shipping

Common Values

ValueCountFrequency (%)
0 106
 
4.5%
Great vitamin whith the best price aaaaaaaaaaaaaaaaaaaaaa&& 20
 
0.9%
I like it a whole lot&Very good seller, fast shipping,recommend&Really liked the products 18
 
0.8%
Perfect fit Arrived early than expected&Perfect fit Arrived early than expected&Perfect fit Arrived early than expected 17
 
0.7%
Excellent&Never received the product nor communication fro seller&Fast shipping 14
 
0.6%
Very niceThank you&excellent item, very rare, excellent seller went got great lengths to assure he had all the correct information, great communicator, perfect transaction in all respects thank you&Item asdescribed, well packed, promptly shipped Great Transaction 14
 
0.6%
Entrega rápida, muito antes do previsto&Really fast delivery Package was well packedsecured&Good stuff, good price, quick shipping satisfied with product 12
 
0.5%
Cute&Nice&Absolutely Perfect 9
 
0.4%
Das Sofa wurde wie neu geliefert und vom Personal sorgfältig aufgestellt Terminabsprache, Persönlicher Kontakt und die Möglichkeit eine Garantieverlängerung zu erwerben sind weitere Argumente für einen Kauf , bei Diesem Händler Die Lieferfristen 812 Wochen lt Beschreibung wurden mit 8 Wochen am unteren Ende bedient Alles in Allem eine 5 Sterne Empfehlung für Produkt, Service , und Abwicklung und Händler &SúperAlles passt&Vielen Dank, gibt nix zu meckern 8
 
0.3%
Great product at a great price, thanks&Perfect, thanks&GreatFasrThanks 8
 
0.3%
Other values (1902) 2125
90.4%

Length

2023-10-27T16:42:52.352644image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 2125
 
3.4%
the 1748
 
2.8%
seller 1271
 
2.0%
great 1244
 
2.0%
fast 1183
 
1.9%
as 1158
 
1.8%
i 1085
 
1.7%
thank 1059
 
1.7%
a 1040
 
1.6%
shipping 989
 
1.6%
Other values (6946) 50487
79.6%

Most occurring characters

ValueCountFrequency (%)
62799
15.2%
e 41920
 
10.2%
a 26240
 
6.4%
t 25143
 
6.1%
i 22297
 
5.4%
r 20853
 
5.1%
s 20068
 
4.9%
n 19105
 
4.6%
o 18841
 
4.6%
l 15452
 
3.7%
Other values (160) 139609
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 316949
76.9%
Space Separator 62799
 
15.2%
Uppercase Letter 21802
 
5.3%
Other Punctuation 9312
 
2.3%
Decimal Number 1145
 
0.3%
Control 256
 
0.1%
Other Letter 64
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 41920
13.2%
a 26240
 
8.3%
t 25143
 
7.9%
i 22297
 
7.0%
r 20853
 
6.6%
s 20068
 
6.3%
n 19105
 
6.0%
o 18841
 
5.9%
l 15452
 
4.9%
d 14783
 
4.7%
Other values (71) 92247
29.1%
Other Letter
ValueCountFrequency (%)
4
 
6.2%
4
 
6.2%
3
 
4.7%
3
 
4.7%
3
 
4.7%
2
 
3.1%
2
 
3.1%
2
 
3.1%
2
 
3.1%
2
 
3.1%
Other values (30) 37
57.8%
Uppercase Letter
ValueCountFrequency (%)
T 2839
13.0%
A 2814
12.9%
I 2300
10.5%
G 1997
 
9.2%
E 1531
 
7.0%
S 1367
 
6.3%
F 928
 
4.3%
P 871
 
4.0%
N 744
 
3.4%
R 675
 
3.1%
Other values (24) 5736
26.3%
Decimal Number
ValueCountFrequency (%)
0 338
29.5%
1 200
17.5%
5 171
14.9%
2 116
 
10.1%
3 101
 
8.8%
4 74
 
6.5%
7 40
 
3.5%
6 39
 
3.4%
8 37
 
3.2%
9 29
 
2.5%
Other Punctuation
ValueCountFrequency (%)
& 6320
67.9%
, 2992
32.1%
Control
ValueCountFrequency (%)
252
98.4%
4
 
1.6%
Space Separator
ValueCountFrequency (%)
62799
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 338509
82.1%
Common 73512
 
17.8%
Cyrillic 232
 
0.1%
Hiragana 29
 
< 0.1%
Han 23
 
< 0.1%
Greek 10
 
< 0.1%
Katakana 7
 
< 0.1%
Hangul 5
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 41920
 
12.4%
a 26240
 
7.8%
t 25143
 
7.4%
i 22297
 
6.6%
r 20853
 
6.2%
s 20068
 
5.9%
n 19105
 
5.6%
o 18841
 
5.6%
l 15452
 
4.6%
d 14783
 
4.4%
Other values (66) 113807
33.6%
Cyrillic
ValueCountFrequency (%)
а 28
 
12.1%
о 26
 
11.2%
е 18
 
7.8%
с 15
 
6.5%
р 14
 
6.0%
в 12
 
5.2%
к 12
 
5.2%
и 11
 
4.7%
п 11
 
4.7%
д 10
 
4.3%
Other values (22) 75
32.3%
Hiragana
ValueCountFrequency (%)
4
13.8%
3
10.3%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
Other values (6) 6
20.7%
Common
ValueCountFrequency (%)
62799
85.4%
& 6320
 
8.6%
, 2992
 
4.1%
0 338
 
0.5%
252
 
0.3%
1 200
 
0.3%
5 171
 
0.2%
2 116
 
0.2%
3 101
 
0.1%
4 74
 
0.1%
Other values (5) 149
 
0.2%
Han
ValueCountFrequency (%)
4
17.4%
3
13.0%
3
13.0%
2
8.7%
2
8.7%
2
8.7%
1
 
4.3%
1
 
4.3%
1
 
4.3%
1
 
4.3%
Other values (3) 3
13.0%
Greek
ValueCountFrequency (%)
α 2
20.0%
ι 2
20.0%
ε 2
20.0%
Κ 1
10.0%
ν 1
10.0%
κ 1
10.0%
σ 1
10.0%
Katakana
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 411750
99.9%
None 281
 
0.1%
Cyrillic 232
 
0.1%
Hiragana 29
 
< 0.1%
CJK 23
 
< 0.1%
Hangul 5
 
< 0.1%
Katakana 5
 
< 0.1%
Latin Ext Additional 1
 
< 0.1%
Phonetic Ext 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
62799
15.3%
e 41920
 
10.2%
a 26240
 
6.4%
t 25143
 
6.1%
i 22297
 
5.4%
r 20853
 
5.1%
s 20068
 
4.9%
n 19105
 
4.6%
o 18841
 
4.6%
l 15452
 
3.8%
Other values (57) 139032
33.8%
None
ValueCountFrequency (%)
ä 51
18.1%
ö 34
12.1%
é 27
9.6%
á 25
8.9%
ü 22
7.8%
í 22
7.8%
ó 20
 
7.1%
è 18
 
6.4%
ú 10
 
3.6%
ã 9
 
3.2%
Other values (20) 43
15.3%
Cyrillic
ValueCountFrequency (%)
а 28
 
12.1%
о 26
 
11.2%
е 18
 
7.8%
с 15
 
6.5%
р 14
 
6.0%
в 12
 
5.2%
к 12
 
5.2%
и 11
 
4.7%
п 11
 
4.7%
д 10
 
4.3%
Other values (22) 75
32.3%
Hiragana
ValueCountFrequency (%)
4
13.8%
3
10.3%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
Other values (6) 6
20.7%
CJK
ValueCountFrequency (%)
4
17.4%
3
13.0%
3
13.0%
2
8.7%
2
8.7%
2
8.7%
1
 
4.3%
1
 
4.3%
1
 
4.3%
1
 
4.3%
Other values (3) 3
13.0%
Latin Ext Additional
ValueCountFrequency (%)
1
100.0%
Phonetic Ext
ValueCountFrequency (%)
1
100.0%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Katakana
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

kmeans_cluster
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size101.3 KiB
1
1259 
0
802 
2
290 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2351
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1259
53.6%
0 802
34.1%
2 290
 
12.3%

Length

2023-10-27T16:42:52.472285image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-27T16:42:52.574855image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1259
53.6%
0 802
34.1%
2 290
 
12.3%

Most occurring characters

ValueCountFrequency (%)
1 1259
53.6%
0 802
34.1%
2 290
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2351
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1259
53.6%
0 802
34.1%
2 290
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1259
53.6%
0 802
34.1%
2 290
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1259
53.6%
0 802
34.1%
2 290
 
12.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size101.3 KiB
0
2061 
1
290 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2351
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2061
87.7%
1 290
 
12.3%

Length

2023-10-27T16:42:52.681900image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-27T16:42:52.819917image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2061
87.7%
1 290
 
12.3%

Most occurring characters

ValueCountFrequency (%)
0 2061
87.7%
1 290
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2351
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2061
87.7%
1 290
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2061
87.7%
1 290
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2061
87.7%
1 290
 
12.3%

no_of_comments
Real number (ℝ)

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6882178
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.3 KiB
2023-10-27T16:42:52.962687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q35
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4232811
Coefficient of variation (CV)0.38589943
Kurtosis-0.3421834
Mean3.6882178
Median Absolute Deviation (MAD)0
Skewness0.64241077
Sum8671
Variance2.0257292
MonotonicityNot monotonic
2023-10-27T16:42:53.114546image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 1459
62.1%
6 498
 
21.2%
4 153
 
6.5%
1 128
 
5.4%
5 72
 
3.1%
7 21
 
0.9%
2 17
 
0.7%
8 2
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
1 128
 
5.4%
2 17
 
0.7%
3 1459
62.1%
4 153
 
6.5%
5 72
 
3.1%
6 498
 
21.2%
7 21
 
0.9%
8 2
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 2
 
0.1%
7 21
 
0.9%
6 498
 
21.2%
5 72
 
3.1%
4 153
 
6.5%
3 1459
62.1%
2 17
 
0.7%
1 128
 
5.4%

Interactions

2023-10-27T16:42:47.608070image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:36.669880image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:37.708418image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:38.731508image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:39.984258image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:41.468984image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:42.738604image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:43.936639image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:45.106773image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:46.409678image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:47.718906image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:36.776032image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:37.811968image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:38.836553image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:40.108276image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:41.624604image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:42.844650image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:44.049990image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:45.227193image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:46.540108image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:47.830716image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:36.876949image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:37.912174image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:38.947075image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:40.257569image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:41.738192image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:42.984556image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:44.155576image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:45.381542image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:46.645665image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:47.967565image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:36.985605image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:38.020848image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:39.114989image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:40.392438image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:41.847106image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:43.148152image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:44.262467image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:45.553368image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:46.754866image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:48.086025image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:37.088411image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:38.118198image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:39.249925image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:40.559845image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:41.954185image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:43.283057image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:44.369856image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:45.702283image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:46.866532image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:48.193804image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:37.182651image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:38.213624image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:39.370534image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:40.722717image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:42.058657image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:43.381150image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:44.499517image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:45.812380image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:46.984771image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:48.305152image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:37.284889image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:38.315393image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:39.493956image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:40.898720image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:42.188757image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:43.486798image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:44.613700image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:45.908922image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:47.105497image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:48.480825image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:37.384317image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:38.418629image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:39.625998image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:41.050987image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:42.325037image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:43.606716image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:44.732996image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:46.031458image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:47.225274image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:48.595045image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:37.485098image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:38.523769image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:39.732806image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:41.170349image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:42.438088image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:43.716816image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:44.853466image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:46.136912image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:47.350682image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:48.731657image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:37.597964image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:38.620914image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:39.861969image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:41.316120image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:42.576145image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:43.826851image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:44.978570image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:46.267794image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-27T16:42:47.488170image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-10-27T16:42:53.234647image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
item_approx_priceaccurate_descriptioncommunicationshipping_speedfeedback_pritem_primary_pricereview_countreasonable_shipping_costitem_nono_of_commentskmeans_clusteragglomerative_cluster
item_approx_price1.0000.0660.0540.222-0.0410.7850.183-0.071-0.0780.0020.2240.267
accurate_description0.0661.0000.6940.6850.101-0.081-0.0170.254-0.0760.1600.7060.998
communication0.0540.6941.0000.7220.181-0.1100.0000.346-0.0920.1720.7060.998
shipping_speed0.2220.6850.7221.0000.1660.0400.0470.241-0.1150.1920.7060.998
feedback_pr-0.0410.1010.1810.1661.000-0.1690.0870.400-0.0480.3560.9840.358
item_primary_price0.785-0.081-0.1100.040-0.1691.0000.190-0.254-0.003-0.1830.2430.258
review_count0.183-0.0170.0000.0470.0870.1901.0000.046-0.0550.0760.0430.062
reasonable_shipping_cost-0.0710.2540.3460.2410.400-0.2540.0461.000-0.0120.4500.7010.990
item_no-0.078-0.076-0.092-0.115-0.048-0.003-0.055-0.0121.000-0.1010.1180.124
no_of_comments0.0020.1600.1720.1920.356-0.1830.0760.450-0.1011.0000.5210.689
kmeans_cluster0.2240.7060.7060.7060.9840.2430.0430.7010.1180.5211.0001.000
agglomerative_cluster0.2670.9980.9980.9980.3580.2580.0620.9900.1240.6891.0001.000

Missing values

2023-10-27T16:42:48.984811image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-27T16:42:49.223729image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

item_approx_priceaccurate_descriptionseller_namecommunicationshipping_speedtitlefeedback_pritem_primary_pricereview_countreasonable_shipping_costitem_nocommentkmeans_clusteragglomerative_clusterno_of_comments
34513.3021134.9health-solution-prime5.04.9Vitamins And Dietary Supplements - Muscle Builder XXL - more muscle growth - 1 B4.5981463.00370005.0334998922302arrived on time&Excellent&Never received the product nor communication fro seller&Fast shipping104
34523.3021134.9health-solution-prime5.04.9Vitamins And Dietary Supplements - MALE VIRILITY - formula works well - 1 Bottle4.5981463.00370005.0333636551366Excellent&Never received the product nor communication fro seller&Fast shipping103
34533.2950964.9health-solution-prime5.04.9Vitamins And Dietary Supplements - GARCINIA CAMBOGIA -Manage cortisol - 1 B4.5981462.99673205.0334895188794Excellent&Never received the product nor communication fro seller&Fast shipping103
34543.4400984.9health-solution-prime5.04.9Vitamins And Dietary Supplements - CREATINE POWDER - INSIDE Your Body - 1 B, 1004.5981463.13983305.0335055451413Excellent&Never received the product nor communication fro seller&Fast shipping103
34554.2724914.9health-solution-prime5.04.9Vitamins And Dietary Supplements - ELK VELVET ANTLER - overall wellness - 1 B,604.5981463.96575305.0335055439038Excellent&Never received the product nor communication fro seller&Fast shipping103
34563.3021134.9health-solution-prime5.04.9Hair growth vitamins - ANTI GRAY HAIR DIETARY SUPPLEMENT- Stimulating growth 1B4.5981463.00370005.0332540421476AA&Really good vitamins&Received item Very Quickly Awesome seller&Excellent&Never received the product nor communication fro seller&Fast shipping106
34573.1871795.0Richisabatin_05.05.0TAMAFLEX Fast Acting Dietary Supplement 28 Vegetarian Capsules New SEALED0.0000002.89037264.8154829167423Fast shipping&all good thank you&Fast shipping&Arrived quickly Well protected while shipped Happy with My purchase &Grewt&Great006
34583.4619794.9Nature Supplements Store5.05.0heart health mens vitamins - OMEGA 3 6 9 3600 MG - omega 3 fish oil 1B0.0000003.16167005.0283472191823Great vitamin whith the best price aaaaaaaaaaaaaaaaaaaaaa&&003
34593.0951255.0Nutriment Boutique4.94.9Berberine 1000mg plus Zinc and Vitamin C 60 Caps Gluten Free4.6101582.79971755.0164671345160New as promised &Great price, Fast delivery&Excellent fast shipping, Highly recommend this vendor &Excelent ítem&Easy transaction Thank you A&Excellent Product106
34603.3410930.0alg_energy_nutra0.00.0Organic Spirulina Capsules Dietary Supplement Nutraceutical Vitamin energy Boost0.0000003.04213900.01860081438300211
item_approx_priceaccurate_descriptionseller_namecommunicationshipping_speedtitlefeedback_pritem_primary_pricereview_countreasonable_shipping_costitem_nocommentkmeans_clusteragglomerative_clusterno_of_comments
57963.9613845.0thedingledog5.05.0Knox Gear 38" Microphone Studio Stand for Yeti and Snowball Microphones NEW/Open4.6141303.65842005.0393708187574Item as described thanks&Wonderful, great condition, fast shipping So happy to have this game&Awesome seller, price and product Fast shipping, good communication Happy with purchase Thanks103
57972.0566850.0sfdreselling0.00.0Knox Gear, Pop Filter (KN-PF1) for Broadcasting & Recording Microphones. NEW!4.4624541.79175940.0325115232680Excellent seller, fast shipping and just as described&fun little book in great shape A&This had arrived in great condition Thanks213
57984.3619515.0Only Amazing Deals5.05.0🔥JUST FOR KIX - MOVE U🔥 2023 Catalogs Cheer Dance Team Praise 130 TOTAL PAGES0.0000004.05664305.0225735608916Excellent seller Lightning fast shipping, very careful packing and great catalogs A&Perfect&Excellent The book arrived quickly and was in perfect condition Thank you003
57993.2921265.0Surplus4Businessllc5.05.01974-1975 VINTAGE CATALOG #874 FALLER HO N MODEL TRAIN and SLOT CARS CATALOG4.6141302.99573204.9155791251287Item better then described super fast awesome affordable shipping, Item packaged really well can tell has been in the business a long time super satisfied thank you five stars &The item arrived quickly and in great shape Thanks&She came safe & sound She was packaged very well Exactly as described & shown Thank you so much seller105
58004.5544034.9Capital Music Gear4.95.0TGV (Train a Grande Vitesse) from Postcards from France4.5961294.24835205.0174343842001Arrived quickly, exactly as ordered Thank you&Arrived quickly, exactly as ordered Thank you&Arrived quickly, exactly as ordered Thank you103
58014.2362785.0lifestylebyfocus5.05.0Knox Gear Professional Microphone Studio Stand for Yeti and Snowball Microphones4.6101583.93163005.0314463447294Item received on time&Great&Great103
58022.6803364.9Sunburst Bargains4.95.02005 Lionel Train Catalog O Gauge and S Gauge, NEW, Model Train Book0.0000002.39516404.7385686163958nice, thanks A&It was in great condition except for this really sticky stuff on front&Fabulous seller and a great transaction003
58030.0000004.9lovehome-zone5.05.0New 80cc Bicycle Motor Bike Motorized 2 Stroke Petrol Gas Engine Silver4.6061704.45434705.0225779747886didnt install yet&Quick service, good price&Great tool, stoked to use it103
58043.3410930.0wildwd0.00.0Morrill Motors unit bearing motor #PSC43E9EGMA2 9W .12 amp 1550rpm 230volt CW Ro0.0000003.04404600.0266303520918Great item and quick delivery Couldnt ask for more&the unit works but the box had water stains 34 up all 4 sidesand the battery was shorted&Perfect213
58051.1118585.0buyersdelight205.05.0New ListingNICOLE EGGERT - STANDING WITH NO CLOTHES ??!!!4.6141300.91629105.0325851258913AAAAAAAAAAAAAAA&AAAAAAAAAAAAAA&Excellent photo dealer question are answer and ship to you right away you can trust them give billion ratings103